Advances of Neural Network Modeling Methods for RF/Microwave Applications
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Advances of Neural Network Modeling Methods for RF/Microwave ApplicationsAbstract
This paper provides an overview of recent advances of neural network modeling techniques which are very useful for RF/microwave modeling and design. First, we review neural network inverse modeling method for fast microwave design. Conventionally, design parameters are obtained using optimization techniques by multiple evaluations of EM-based models, which take a long time. To avoid this problem, neural network inverse models are developed in a special way, such that they provide design parameters quickly for a given specification. The method is used to design complex waveguide dual mode filters and design parameters are obtained faster than the conventional EM-based technique while retaining comparable accuracy. We also review recurrent neural network (RNN) and dynamic neural network (DNN) methods. Both RNN and DNN structures have the dynamic modeling capabilities and can be trained to learn the analog nonlinear behaviors of the original microwave circuits from input-output dynamic signals. The trained neural networks become fast and accurate behavioral models that can be subsequently used in systemlevel simulation and design replacing the CPUintensive detailed representations. Examples of amplifier and mixer behavioral modeling using the neural-network-based approach are also presented.
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References
Q. J. Zhang, K. C. Gupta, and V. K.
Devabhaktuni, “Artificial neural networks for
RF and microwave design-from theory to
practice,” IEEE Trans. Microwave Theory and
Tech., vol. 51, pp. 1339–1350, Apr. 2003.
Q. J. Zhang and K. C. Gupta, Neural Networks
for RF and Microwave Design, Artech House,
Boston, 2000.
P. M. Watson and K. C. Gupta, “EM-ANN
models for microstrip vias and interconnects
in dataset circuits,” IEEE Trans. Microwave
Theory and Tech., vol. 44, pp. 2495–2503,
Dec. 1996.
B. Davis, C. White, M. A. Reece, M. E. Jr.
Bayne, W. L. Thompson, II, N. L. Richardson,
and L. Jr. Walker, “Dynamically configurable
pHEMT model using neural networks for
CAD,” IEEE MTT-S Int. Microwave Symp.
Dig., vol. 1, pp. 177–180, June 2003.
M. Isaksson, D. Wisell, and D. Ronnow,
“Wide-band dynamic modeling of power
amplifiers using radial-basis function neural
networks,” IEEE Trans. Microwave Theory
and Tech., vol. 53, no. 11, pp. 3422–3428,
Nov. 2005.
H. Kabir, Y. Wang, M. Yu, and Q. J. Zhang,
“Applications of artificial neural network
techniques in microwave filter modeling,
optimization and design,” Progress In
Electromagnetic Research Symposium,
Beijing, China, pp. 1972–1976, March 2007.
P. Burrascano, M. Dionigi, C. Fancelli, and M.
Mongiardo, “A neural network model for
CAD and optimization of microwave filters,”
IEEE MTT-S Int. Microwave Symp. Dig.,
Baltimore, MD, vol. 1, pp. 13–16, June 1998.
H. Kabir, Y. Wang, M. Yu and Q. J. Zhang,
“Neural network inverse modeling and
applications to microwave filter design” IEEE
Trans. Microwave Theory and Tech., vol. 56,
no. 4, pp. 867–879, Apr. 2008.
Y. Wang, M. Yu, H. Kabir, and Q. J. Zhang,
“Effective design of cross-coupled filter using
neural networks ad coupling matrix,” IEEE
MTT-S Int. Microwave Symp. Dig., pp. 1431–
, June 2006.
ACES JOURNAL, VOL. 25, NO. 5, MAY 2010
M. M. Vai, S. Wu, B. Li, and S. Prasad,
“Reverse modeling of microwave circuits
with bidirectional neural network models,”
IEEE Trans. Microwave Theory and Tech.,
vol. 46, pp. 1492–1494, Oct. 1998.
Q. J. Zhang and Y. Cao, “Time-domain
neural network approaches to EM modeling
of microwave components,” Springer
Proceedings in Physics: Time Domain
Methods in Electrodynamics, pp. 41–53, Oct.
H. Sharma and Q. J. Zhang, “Automated time
domain modeling of linear and nonlinear
microwave circuits using recurrent neural
networks,” Int. J. RF Microwave Comput.-
Aided Eng., vol.18, no. 3, pp. 195–208, May
Y. H. Fang, M. C. E. Yagoub, F. Wang, and
Q. J. Zhang, “A new macromodeling
approach for nonlinear microwave circuits
based on recurrent neural network,” IEEE
Trans. Microwave Theory Tech., vol. 48, no. 12,
pp. 2335–2344, Dec. 2000.
J. J. Xu, M. Yagoub, R. T. Ding, and Q. J.
Zhang, “Neural based dynamic modeling of
nonlinear microwave circuits,” IEEE Trans.
Microwave Theory Tech., vol. 50, pp. 2769–
, Dec. 2002.
J. Xu, M. C. E. Yagoub, R. Ding, and Q. J.
Zhang, “Exact adjoint sensitivity analysis for
neural-based microwave modeling and
design,” IEEE Trans. Microwave Theory and
Tech., vol. 51, pp. 226–237, Jan. 2003.
S. Haykin, Neural Networks: A
Comprehensive Foundation, 2nd ed.,
Prentice-Hall, pp. 751–756, 1999.
J. Wood, D. E. Root, and N. B. Tufillaro, “A
behavioral modeling approach to nonlinear
model-order reduction for RF/microwave ICs
and systems,” IEEE Trans. Microwave
Theory Tech., vol. 52, no. 9, pp. 2274–2284,
Sep. 2004.
Y. Cao, L. Zhang, J. J. Xu and Q. J. Zhang,
“Efficient harmonic balance simulation of
nonlinear microwave Circuits with dynamic
neural models,” IEEE MTT-S Int.
Microwave Symp. Dig., , pp. 1423–1426,
June 2006.
Q. J. Zhang, NeuroModelerPlus, Department
of Electronics, Carleton University, K1S
B6, Canada.
Advanced Design System (ADS) ver. 2006A,
Agilent Technologies, Inc., 2006
